55745
2021
2021
eng
1
17
17
11
article
MDPI
Basel, Schweiz
1
2021-12-07
2021-12-07
--
Detection of Rare Earth Elements in Minerals and Soils by Laser-Induced Breakdown Spectroscopy (LIBS) Using Interval PLS
The numerous applications of rare earth elements (REE) has lead to a growing global demand and to the search for new REE deposits. One promising technique for exploration of these deposits is laser-induced breakdown spectroscopy (LIBS). Among a number of advantages of the technique is the possibility to perform on-site measurements without sample preparation. Since the exploration of a deposit is based on the analysis of various geological compartments of the surrounding area, REE-bearing rock and soil samples were analyzed in this work. The field samples are from three European REE deposits in Sweden and Norway. The focus is on the REE cerium, lanthanum, neodymium and yttrium. Two different approaches of data analysis were used for the evaluation. The first approach is univariate regression (UVR). While this approach was successful for the analysis of synthetic REE samples, the quantitative analysis of field samples from different sites was influenced by matrix effects. Principal component analysis (PCA) can be used to determine the origin of the samples from the three deposits. The second approach is based on multivariate regression methods, in particular interval PLS (iPLS) regression. In comparison to UVR, this method is better suited for the determination of REE contents in heterogeneous field samples. View Full-Text
Minerals
10.3390/min11121379
2075-163X
Beitz, Toralf
1379
<a href="https://doi.org/10.25932/publishup-55746">Zweitveröffentlichung in der Schriftenreihe Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 1254</a>
WOS:000737018000001
Beitz, T (corresponding author), Univ Potsdam, Phys Chem, D-14476 Potsdam, Germany., rethfeldt@uni-potsdam.de; pbrinkma@uni-potsdam.de; <br /> daniel.riebe@alumni.uni-potsdam.de; beitz@uni-potsdam.de; <br /> nicole.koellner@gfz-potsdam.de; uwe@geo.uni-potsdam.de; <br /> loeh@chem.uni-potsdam.de
State of Brandenburg (ILB) in the LIBSqORE project [80172489]; InfraFEI grant of the State of Brandenburg (ILB) in the FuSeSE project [85045759]; Federal Ministry of Education and Research (BMBF)Federal Ministry of Education & Research (BMBF); Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG); University of Potsdam
CC-BY - Namensnennung 4.0 International
Nina Rethfeldt
Pia Brinkmann
Daniel Riebe
Toralf Beitz
Nicole Köllner
Uwe Altenberger
Hans-Gerd Löhmannsröben
eng
uncontrolled
LIBS
eng
uncontrolled
rare earth elements
eng
uncontrolled
minerals
eng
uncontrolled
PCA
eng
uncontrolled
iPLS regression
Geowissenschaften
Institut für Chemie
Referiert
Publikationsfonds der Universität Potsdam
Gold Open-Access
55746
2021
2022
eng
1
17
17
postprint
Universitätsverlag Potsdam
Potsdam
1
2022-07-22
2022-07-22
--
Detection of Rare Earth Elements in Minerals and Soils by Laser-Induced Breakdown Spectroscopy (LIBS) Using Interval PLS
The numerous applications of rare earth elements (REE) has lead to a growing global demand and to the search for new REE deposits. One promising technique for exploration of these deposits is laser-induced breakdown spectroscopy (LIBS). Among a number of advantages of the technique is the possibility to perform on-site measurements without sample preparation. Since the exploration of a deposit is based on the analysis of various geological compartments of the surrounding area, REE-bearing rock and soil samples were analyzed in this work. The field samples are from three European REE deposits in Sweden and Norway. The focus is on the REE cerium, lanthanum, neodymium and yttrium. Two different approaches of data analysis were used for the evaluation. The first approach is univariate regression (UVR). While this approach was successful for the analysis of synthetic REE samples, the quantitative analysis of field samples from different sites was influenced by matrix effects. Principal component analysis (PCA) can be used to determine the origin of the samples from the three deposits. The second approach is based on multivariate regression methods, in particular interval PLS (iPLS) regression. In comparison to UVR, this method is better suited for the determination of REE contents in heterogeneous field samples. View Full-Text
Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
10.25932/publishup-55746
urn:nbn:de:kobv:517-opus4-557469
1866-8372
Beitz, Toralf
1379
Version of record
<a href="http://publishup.uni-potsdam.de/55745">Bibliographieeintrag der Originalveröffentlichung/Quelle</a>
false
true
CC-BY - Namensnennung 4.0 International
Nina Rethfeldt
Pia Brinkmann
Daniel Riebe
Toralf Beitz
Nicole Köllner
Uwe Altenberger
Hans-Gerd Löhmannsröben
Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
1254
eng
uncontrolled
LIBS
eng
uncontrolled
rare earth elements
eng
uncontrolled
minerals
eng
uncontrolled
PCA
eng
uncontrolled
iPLS regression
Geowissenschaften
open_access
Institut für Chemie
Referiert
Green Open-Access
Universität Potsdam
https://publishup.uni-potsdam.de/files/55746/pmnr1254.pdf
44007
2019
2019
eng
16
786
postprint
1
2019-12-05
2019-12-05
--
Comparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture
The lack of soil data, which are relevant, reliable, affordable, immediately available, and sufficiently detailed, is still a significant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within fields, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, different calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural fields in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The first method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both fields. The second univariate model derived the calibration from the reference analytics of all samples from one field. The prediction is validated by LIBS data of the second field. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the first field are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the first field and independently on the second field data. The second univariate method yielded better calibration and prediction results compared to the first method, since matrix effects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method.
Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe
10.25932/publishup-44007
urn:nbn:de:kobv:517-opus4-440079
1866-8372
5244
Sensors 19 (2019) 23, Art. 5244 DOI: 10.3390/s19235244
<a href="http://publishup.uni-potsdam.de/44006">Bibliographieeintrag der Originalveröffentlichung/Quelle</a>
CC-BY - Namensnennung 4.0 International
Daniel Riebe
Alexander Erler
Pia Brinkmann
Toralf Beitz
Hans-Gerd Löhmannsröben
Robin Gebbers
Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
786
eng
uncontrolled
laser-induced breakdown spectroscopy
eng
uncontrolled
LIBS
eng
uncontrolled
proximal soil sensing
eng
uncontrolled
soil nutrients
eng
uncontrolled
elemental composition
Ingenieurwissenschaften und zugeordnete Tätigkeiten
open_access
Institut für Chemie
Referiert
Open Access
Universität Potsdam
https://publishup.uni-potsdam.de/files/44007/pmnr786.pdf
44006
2019
2019
eng
16
23
19
article
MDPI
Basel
1
2019-11-28
2019-11-28
--
Comparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture
The lack of soil data, which are relevant, reliable, affordable, immediately available, and sufficiently detailed, is still a significant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within fields, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, different calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural fields in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The first method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both fields. The second univariate model derived the calibration from the reference analytics of all samples from one field. The prediction is validated by LIBS data of the second field. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the first field are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the first field and independently on the second field data. The second univariate method yielded better calibration and prediction results compared to the first method, since matrix effects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method.
Sensors
10.3390/s19235244
1424-8220
5244
Universität Potsdam
PA 2019_128
1460.89
<a href="https://doi.org/10.25932/publishup-44007">Zweitveröffentlichung in der Schriftenreihe Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 786</a>
false
false
CC-BY - Namensnennung 4.0 International
Daniel Riebe
Alexander Erler
Pia Brinkmann
Toralf Beitz
Hans-Gerd Löhmannsröben
Robin Gebbers
eng
uncontrolled
laser-induced breakdown spectroscopy
eng
uncontrolled
LIBS
eng
uncontrolled
proximal soil sensing
eng
uncontrolled
soil nutrients
eng
uncontrolled
elemental composition
Ingenieurwissenschaften und zugeordnete Tätigkeiten
open_access
Institut für Chemie
Referiert
Publikationsfonds der Universität Potsdam
Open Access
58964
2020
2020
eng
17
18
20
article
MDPI
Basel
1
2020-09-09
2020-09-09
--
Classification of copper minerals by handheld laser-induced breakdown spectroscopy and nonnegative tensor factorisation
Laser-induced breakdown spectroscopy (LIBS) analysers are becoming increasingly common for material classification purposes. However, to achieve good classification accuracy, mostly noncompact units are used based on their stability and reproducibility. In addition, computational algorithms that require significant hardware resources are commonly applied. For performing measurement campaigns in hard-to-access environments, such as mining sites, there is a need for compact, portable, or even handheld devices capable of reaching high measurement accuracy. The optics and hardware of small (i.e., handheld) devices are limited by space and power consumption and require a compromise of the achievable spectral quality. As long as the size of such a device is a major constraint, the software is the primary field for improvement. In this study, we propose a novel combination of handheld LIBS with non-negative tensor factorisation to investigate its classification capabilities of copper minerals. The proposed approach is based on the extraction of source spectra for each mineral (with the use of tensor methods) and their labelling based on the percentage contribution within the dataset. These latent spectra are then used in a regression model for validation purposes. The application of such an approach leads to an increase in the classification score by approximately 5% compared to that obtained using commonly used classifiers such as support vector machines, linear discriminant analysis, and the k-nearest neighbours algorithm.
Sensors
10.3390/s20185152
32917027
1424-8220
outputup:dataSource:PubMed:2020
5152
WOS:000581205200001
Zdunek, R (corresponding author), Wroclaw Univ Sci & Technol, Fac Elect, Dept Field Theory Elect Circuits & Optoelect, Wybrzeze Wyspianskiego 27, PL-50370 Wroclaw, Poland., michal.wojcik@pwr.edu.pl; pbrinkma@uni-potsdam.de; <br /> rafal.zdunek@pwr.edu.pl; riebe@uni-potsdam.de; beitz@uni-potsdam.de; <br /> sven.merk@ltb-berlin.de; katarzyna.cieslik@ltb-berlin.de; <br /> david.mory@ltb-berlin.de; arkadiusz.antonczak@pwr.edu.pl
German federal state of Brandenburg; European Regional Development Fund; (ERDF 2014-2020); economic development agency Brandenburg (WFBB) in the; LIBSqORE project [80172489]
Zdunek, Rafal
2023-04-24T08:00:39+00:00
sword
importub
filename=package.tar
6cdc7685411ce45f15a9901695e211b5
2052857-7
false
true
CC-BY - Namensnennung 4.0 International
Michal Wojcik
Pia Brinkmann
Rafał Zdunek
Daniel Riebe
Toralf Beitz
Sven Merk
Katarzyna Cieslik
David Mory
Arkadiusz Antonczak
eng
uncontrolled
LIBS
eng
uncontrolled
NTF
eng
uncontrolled
HALS
eng
uncontrolled
classification
eng
uncontrolled
copper minerals
Physik
Chemie und zugeordnete Wissenschaften
Institut für Chemie
Referiert
Import
Gold Open-Access
DOAJ gelistet